Method, system and computer readable medium for predictive hypoglycemia detection for mild to moderate exercise

ABSTRACT

A system for generating a hypoglycemia risk signal associated with exercise-induced Hypoglycemia. The system can include a processor configured to obtain a blood glucose signal (BG start ), a ratio of absolute insulin on board over total daily insulin signal (IOB abs /TDI), and an initial glycemic slope signal (S 0 ); generate a hypoglycemia risk signal based on a hypoglycemia prediction algorithm that determines the probability of a user being hypoglycemic during or after exercise based on the obtained BG starts , IOB abs /TDI and S 0 .

FIELD

A system, method and non-transient computer readable medium forpredicting exercise-induced hypoglycemia in patients with diabetes.

BACKGROUND

Regular physical activity has a positive impact on the quality of lifeand decreases cardiovascular risk factors and mortality. However, fearof hypoglycemia is the strongest barrier to regular exercise in patientswith type 1 diabetes. Physical activity has been known for increasingthe risk of hypoglycemia for people with type 1 diabetes.

To maintain their blood glucose levels in the normal range, type 1diabetes patients are required to balance insulin doses and food intakeon a regular basis. The exogenous insulin leads to a hyperinsulinemiestate during exercise which is due to the absence of a physiologicaldecrease in insulin secretion and an increase in the absorption ofpreviously injected insulin. Hyperinsulinemia, coupled with impairedcounterregulatory response and an imbalance between hepatic glucoseproduction and muscle glucose disposal, leads to hypoglycemia.

The fear of hypoglycemia generally leads to overcompensation ofadditional intake of carbohydrates and/or excessive insulin dosesreductions. For better glycemic control actions, patients with type 1diabetes need to be informed on the risk of hypoglycemia associated withexercise

There remains a need for a predictive hypoglycemia model for patientswith type 1 diabetes who intend to exercise at a moderate intensitylevel. There also remains a need for a model that can be used as thefoundation for a predictive hypoglycemia classifier. There also remainsa need for decision support systems to mitigate hypoglycemia risk by,for example, enabling manual and automated adjustments to insulindelivery, recommending carbohydrate consumption, or recommending thatexercise be postponed until the risk of exercise-induced hypoglycemia isreduced.

SUMMARY

An aspect of an embodiment of the present invention provides a system,method, and non-transient computer readable medium for, among otherthings, predicting exercise-induced hypoglycemia in patients prior toexercise.

An aspect of an embodiment of the present invention provides for, amongother things, implementing an initial step that may involve obtaining ablood glucose signal and/or an initial glycemic slope signal, which maybe obtained front a continuous glucose monitor. See also, for example,U.S. patent application Ser. No. 14/241,383 entitled “Method, System andComputer Readable Medium for Adaptive Advisory Control of Diabetes”,filed Feb. 26, 2014; International Patent Application NoPCT/US2012/052422 entitled “Method, System and Computer Readable Mediumfor Adaptive Advisory Control of Diabetes”, filed Aug. 26, 2012, andInternational Patent Application Publication No. WO 2013/032065, Mar. 7,2013, all of which are hereby incorporated by reference in theirentirety herein (and which are not admitted to be prior art with respectto the present invention by inclusion in this section).

An aspect of various embodiments of the present invention determines ahypoglycemia risk signal with a hypoglycemia prediction algorithm, anddetermines a hypoglycemia risk state with a classifier algorithm thatclassifies the hypoglycemia risk signal to identify an actionablehypoglycemia risk state based on a predefined threshold. In variousembodiments, the actionable hypoglycemia risk state is a “riskassessment” (i.e., not a probability determination, for example) andenables mitigation opportunities for the user, either through manual orautomated methods.

An aspect of various embodiments of the present invention is responsiveto an indicated risk of hypoglycemia during and after exercise. Invarious embodiments, the present invention generates an alert,communicates an instruction to a user, communicates an instruction to aninsulin pump, and/or communicate an instruction to a pump containing aglucose increasing drug, responsive to an indicated risk of hypoglycemiaduring or after exercise.

In various embodiments of the present invention, the hypoglycemiaprediction algorithm determines the probability of a user beinghypoglycemic during or after exercise based on the blood glucose signal,the ratio of insulin on board over total daily insulin signal, and theinitial glycemic slope signal. In various embodiments, the hypoglycemiaprediction algorithm also determines the probability of a user beinghypoglycemic during or after exercise based on one or more other signalsincluding: the type of exercise, the intensity of exercise, and theduration of exercise to be performed.

In various embodiments, the hypoglycemia prediction algorithm alsodetermines the probability of a user being hypoglycemic during or afterexercise based on one or more other signals including: the relativeinsulin on board (lOB) as an indicator of the remaining insulin in thebloodstream (calculated taking into account the 4 hour insulininjections history and subtracting the basal injections), the absoluteinsulin on board (IOB_(abs), absolute refers to the fact that allinjections are taken into account in the 4 hour insulin injectionshistory), the total daily insulin (TDI), the ratio lOB_(abs)/TDI as anindicator of body insulin exposure, the ratio TDI/BW (where BW is thebody weight), the age (as a continuous or categorical variable), thebody weight (BW) and the gender.

An aspect of an embodiment of the present invention provides a system,method and computer readable medium that, among other things, defines analgorithmic architecture for control of diabetes that includes both liveand retrospective analysis of data. See also, for example, U.S. patentapplication Ser. No. 13/322,943 entitled “System Coordinator and ModularArchitecture for Open-Loop and Closed-Loop Control of Diabetes”, filedNov. 29, 2011; U.S. Patent Application Publication No. 2012/0078067.Mar. 29, 2012; International Patent Application No. PCT/US2010/036629entitled “System Coordinator and Modular Architecture for Open-Loop andClosed-Loop Control of Diabetes”, filed May 28, 2010; and InternationalPatent Application Publication No WO 2010/158848. Dec. 2, 2010, all ofwhich are hereby incorporated by reference in their entirety herein (andwhich are not admitted to be prior art with respect to die presentinvention by inclusion in this section). An aspect of variousembodiments of the present invention (system, method and computerreadable medium) may provide a number of novel and nonobvious features,elements and characteristics, such as but not limited thereto, asfollows creating and applying algorithms (and techniques and methods)for retrospective analysis. Moreover, such algorithms (and techniquesand methods) may be implemented by being integrated (e.g. “plug into”)the modular architecture of the system or device.

An aspect of various embodiments of the present invention (system,method and computer readable medium) may provide a number of novel andnonobvious features, elements and characteristics, such as but notlimited thereto, the following: (i) it uses CGM (which may in turn beenhanced by knowledge of finger stick values, of which may be optionaland not necessarily essential), (ii) it addresses both hypoglycemia andhyperglycemia, (iii) it produces a “risk assessment” (i.e. not aprobability determination, fix example), (iv) it provides riskassessments that are broken down into (a) “unaddressable risk” where thehistorical record indicates a tendency to both hypo and hyperglycemia ata given time of the day, (b) actionable hypoglycemia risk, and (c)actionable hyperglycemia risk.

The devices, systems, non-transitory computer readable medium, andmethods of various embodiments of the invention disclosed herein mayutilize aspects disclosed in “Decision Support System for TIDM patients'safety during and immediately after a mile to moderate physicalactivity”, the attached Appendix 1, and “Predictive Hypoglycemiadetection classifier for mild to moderate exercise in type 1 Diabetes”,attached as Appendix 2, both of which are hereby incorporated byreference herein in their entirety (and which are not admitted to beprior an with respect to the present invention by inclusion in thissection).

BRIEF DESCRIPTION OF THE DRAWINGS

The invention can be best understood from the following detaileddescription of exemplary embodiments of the invention taken inconjunction with the accompanying drawings.

FIG. 1 is a graph of test results for an embodiment of the invention.

FIG. 2 is a block diagram of an example of a machine upon which one ormore aspects of embodiments of the present invention can be implemented

FIG. 3 is a high level functional block diagram of an embodiment of theinvention.

FIG. 4A is a block diagram of a computing device upon which one or moreaspects of embodiments of the invention can be implemented.

FIG. 4B illustrates a network system upon which one or more aspects ofembodiments of the invention can be implemented.

FIG. 5 illustrates a system in which one or more embodiments of theinvention can be implemented using a network, or portion of a network orcomputers.

DETAILED DESCRIPTION OF THE DRAWINGS

This invention provides a method, system, and computer readable mediumfor, among other things, predicting the hypoglycemic risk (or mild tomoderate exercise for patients with type 1 diabetes.

In view of the many possible variations within the spirit of theinvention, the invention will be discussed with reference to exemplaryembodiments. However, it will be appreciated by those skilled in the artthat the allowing discussion is for demonstration purposes, and shouldnot be interpreted as a limitation of the invention. Other variationswithout departing from the spirit of the invention are applicable.

A method and system for predicting exercise-induced hypoglycemia for auser are presently disclosed. In an embodiment, a system includes adigital processor and an exercise module in communication with thedigital processor configured to implement the disclosed method. In someembodiments, the method includes obtaining a blood glucose signal(BG_(start)), a ratio of absolute insulin on board over total dailyinsulin signal (IOB_(abs)/TDI), and an initial glycemic slope signal(S0). The method also includes determining a hypoglycemia risk signalwith a hypoglycemia prediction algorithm that determines the probabilityof the user being hypoglycemic during or after exercise based on theblood glucose signal, the ratio of absolute insulin on board over totaldaily insulin signal, and the initial glycemic slope signal; anddetermining a hypoglycemia risk state with a classifier algorithm thatclassifies the hypoglycemia risk signal to identify an actionablehypoglycemia risk state based on a predefined threshold.

In an embodiment, the response variable H is obtained by applying athreshold blood glucose level (BG_(thresh)) on the actual blood glucosevalue at the end of exercise (BG_(end)).

$H = \left\{ \begin{matrix}{1,} & {{BG}_{end} < {BG}_{thresh}} \\{0,} & {{BG}_{end} \geq {BG}_{thresh}}\end{matrix} \right.$

In one embodiment, the hypoglycemia prediction algorithm is the legittransform of the probability of being hypoglycemic during or afterexercise, and is defined by the equation:

${{Logit}(P)} = {{\beta \; 0} + {\beta_{1} \cdot {BG}_{start}} + {\beta_{2} \cdot \frac{{IOB}_{abs}}{TDI}} + {\beta_{3} \cdot S_{0}}}$${Where}\left\{ \begin{matrix}{{{Logit}(P)} = {{Log}\left( \frac{P}{1 - P} \right)}} \\{P = \frac{e^{{\beta \; 0} + {\beta_{1} \cdot {BG}_{start}} + {\beta_{2} \cdot \frac{{IOB}_{abs}}{TDI}} + {\beta_{3} \cdot S_{0}}}}{1 + e^{{\beta \; 0} + {\beta_{1} \cdot {BG}_{start}} + {\beta_{2} \cdot \frac{{IOB}_{abs}}{TDI}} + {\beta_{3} \cdot S_{0}}}}}\end{matrix} \right.$

In one embodiment, the coefficient β₀, β₁, β₂, β₃ are 8.682, −0.082,69.572, and −1.869, respectively. In other embodiments, the hypoglycemiaprediction algorithm may include additional or different terms, withdifferent predetermined coefficients such that the hypoglycemia risksignal is based on additional signal as discussed below.

After the hypoglycemia risk signal is determined using the hypoglycemiaprediction algorithm, a hypoglycemia risk state is determined using aclassifier algorithm. The classifier algorithm identities an actionablehypoglycemia risk state based on a predefined threshold (DET_(thresh)),which may be communicated to the user or used in controlling insulindelivery to mitigate the hypoglycemic risk.

In one embodiment, a the classifier algorithm is:

${{Logit}(P)} = \left\{ \begin{matrix}{1,} & {{{Logit}(P)} \geq {DET}_{thresh}} \\{0,} & {{{Logit}(P)} < {DET}_{thresh}}\end{matrix} \right.$

With this classifier algorithm a value of 1 indicates a risk ofhypoglycemia following the intended exercise. In one embodiment, theDETthresh is 0.4.

In other embodiments, the classifier algorithm may classify two or moreactionable hypoglycemia risk states, each having a recommended actionfor the user, such as, reducing or stopping insulin delivery, consuminga defined amount of carbohydrates, or postponing the intended exercise.

In various embodiments, an alert is generated if the hypoglycemia riskstate indicates a risk of hypoglycemia during or after exercise. Thealert may be a visual or audible alert. In one embodiment, the alert isdisplayed on the display of a portable computing device. In otherembodiments, the alert may be communicated to the user through email,text message, or other methods as desired.

In yet another embodiments, if the hypoglycemia risk state indicates arisk of hypoglycemia during or after exercise, an instruction iscommunicated to an insulin pump. In one example, the instruction causesthe pump to discontinue supplying insulin for a determined period oftime to mitigate risk of hypoglycemia during and after the intendedexercise.

In yet another embodiment, the user is instructed to consume adetermined amount of carbohydrates in order to mitigate the risk ofhypoglycemia during or after exercise.

In yet another embodiment, the user is instructed to increase the amountof a glucose increasing drug (e.g. glucagon).

In yet another embodiment, the user or the injection device is presentedwith a combination of advice signals described in paragraphs [0033],[0034], and [0035]

The disclosed system and method may receive input signals from the userand/or from a variety of sensors. In one embodiment, the blood glucosesignal is received from a continuous blood glucose monitor. The initialglycemic slope signal may be obtained based on the received bloodglucose signal or may be computed by the continuous blood glucosemonitor and provided as a separate signal. In either case, the initialglycemic slope signal identities the trend of the blood glucose levelprior to starting exercise.

A user may employ the disclosed system and method prior to startingexercise in order to assess the risk of being hypoglycemic during orafter exercise. In some embodiments, one or more sensors may be used todetect exercise so that a determination of the post-exercisehypoglycemic risk may be provided automatically. In one embodiment, anactivity signal is obtained from at least one sensor configured todetect when the user beings to exercise. A heartrate sensor may be usedto detect an increase in heartrate associated with exertion related tothe beginning of exercise or other physical activity. In someembodiments, an accelerometer, which may include a multi-axisaccelerometer may be used to detect movement associated with exercise.In response to detecting that the use has begun to exercise, thehypoglycemia risk state is determined. If the hypoglycemia risk stateindicates a risk of hypoglycemia during or after exercise, an alert maybe generated and a recommendation provided to the user, such as,discontinue exercise or consume carbohydrates to mitigate the risk ofhypoglycemia.

The disclosed system and method may also utilize additional factors todetermine the risk of hypoglycemia during or after exercise. A user mayprovide additional information, such as one or more of the type ofexercise, the intensity of the exercise, and the planned duration of theexercise to be performed. In some embodiments, the hypoglycemiaprediction algorithm may therefore determine the probability of the userbeing hypoglycemic during or after exercise based at least in part onthis additional information. In yet other embodiments, the classifieralgorithm may classify the risk of hypoglycemia based at least in parton this additional information, such as by reducing the predefinedthreshold at which an actionable risk of hypoglycemia is indicated.

An embodiment of the presently disclosed method was tested, and theresults are illustrated in FIG. 1. As shown, the blood glucose levels ofmultiple patients were measured after exercise (Post exercise SMBG) andplotted versus the patient's blood glucose level prior to exercise (Preexercise SMBG). The patients were divided into a control group (CNTL),and a test group (TX) utilizing the presently disclosed method. At thetime of the exercise, the test subjects used the method to determine ahypoglycemia risk slate and received one of our recommendations based onthe determined hypoglycemia risk state. The test subjects wereinstructed to do nothing if a hypoglycemia risk state was notdetermined, and to stop insulin for two hours if a hypoglycemia riskstate was determined.

As shown in FIG. 1, the blood glucose level of the control groupdeclined at a substantially taster rate than the test group. In otherwords, the determination of hypoglycemia risk state enabled the testgroup to modify their insulin regime to mitigate the risk ofhypoglycemia after moderate exercise. Accordingly, these resultsdemonstrate that the disclosed system and method enables patients withtype 1 diabetes to benefit from exercise while reducing the risk ofexercise induced hypoglycemia.

FIG. 2 is a block diagram illustrating an example of a machine uponwhich one or more aspects of embodiments of the present invention can beimplemented.

FIG. 2 illustrates a block diagram of an example machine 400 upon whichone or more embodiments (e.g. discussed methodologies) can beimplemented (e.g., run).

Examples of machine 400 can include logic, one or more components,circuits (e.g., modules), or mechanisms. Circuits are tangible entitiesconfigured to perform certain operations. In an example, circuits can bearranged (e g., internally or with respect to external entities such asother circuits) in a specified manner. In an example, one or morecomputer systems (e.g., a standalone, client or server computer system)or one or more hardware processors (processors) can be configured bysoftware (e.g., instructions, an application portion, or an application)as a circuit that operates to perform certain operations as describedherein In an example, the software can reside (1) on a non-transitorymachine readable medium or (2) in a transmission signal. In an example,the software, when executed by the underlying hardware of the circuit,causes the circuit to perform the certain operations.

In an example, a circuit can be implemented mechanically orelectronically. For example, a circuit can comprise dedicated circuitryor logic that is specifically configured to perform one or moretechniques such as discussed above, such as including a special-purposeprocessor, a field programmable gate array (FPGA) or anapplication-specific integrated circuit (ASIC). In an example, a circuitcan comprise programmable logic (e.g., circuitry, as encompassed withina general-purpose processor or other programmable processor) that can betemporarily configured (e.g., by software) to perform the certainoperations. It will be appreciated that the decision to implement acircuit mechanically (e.g., in dedicated and permanently configuredcircuitry), or in temporarily configured circuitry (e.g., configured bysoftware) can be driven by cost and time considerations.

Accordingly, the term “circuit” is understood to encompass a tangibleentity, be that an entity that is physically constructed, permanentlyconfigured (e.g., hardwired), or temporarily (e.g., transitorily)configured (e.g., programmed) to operate in a specified manner or toperform specified operations. In an example, given a plurality oftemporarily configured circuits, each of the circuits need not beconfigured or instantiated at any one instance in time. For example,where the circuits comprise a general-purpose processor configured viasoftware, the general-purpose pocessor can be configured as respectivedifferent circuits at different times. Software can accordinglyconfigure a processor, for example, to constitute a particular circuitat one instance of time and to constitute a different circuit at adifferent instance of time.

In an example, circuits can provide information to, and receiveinformation from, other circuits. In this example, the circuits can beregarded as being communicatively coupled to one or more other circuits.Where multiple of such circuits exist contemporaneously, communicationscan be achieved through signal transmission (e.g., over appropriatecircuits and buses) that connect the circuits. In embodiments in whichmultiple circuits are configured or instantiated at different times,communications between such circuits can be achieved, for example,through the storage and retrieval of information in memory structures towhich the multiple circuits have access. For example, one circuit canperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further circuit canthen, at a later time, access the memory device to retrieve and processthe stored output. In an example, circuits can be configured to initiateor receive communications with input or output devices and can operateon a resource (e.g., a collection of information).

The various operations of method examples described herein can beperformed, at least partially, by one or more processors that eretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors can constitute processor-implementedcircuits that operate to perform one or more operations or functions. Inan example, the circuits referred to herein can compriseprocessor-implemented circuits.

Similarly, the methods described herein can be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod can be performed by one or processors or processor-implementedcircuits. The performance of certain of the operations can bedistributed among the one or more processors, not only residing within asingle machine, but deployed across a number of machines. In an example,the processor or processors can be located in a single location (e.g.,within a home environment, an office environment or as a server farm),while in other examples the processors can be distributed across anumber of locations.

The one or more processors can also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoptions can be performed by a group of computers (as examples ofmachines including processors), with these operations being accessiblevia a network (e.g., the Internet) and via one or more appropriateinterfaces (e.g., Application Program Interfaces (APIs).)

Example embodiments (e.g., apparatus, systems, or methods) can beimplemented in digital electronic circuitry, in computer hardware, infirmware, in software, or in any combination thereof. Exampleembodiments can be implemented using a computer program product (e.g., acomputer program, tangibly embodied in an information carrier or in amachine readable medium, for execution by, or to control the operationof, data processing apparatus such as a programmable processor, acomputer, or multiple computers).

A computer program can be written in any form of programming language,including compiled or interpreted languages, and it can be deployed inany form, including as a stand-alone program or as a software module,subroutine, or other unit suitable for use in a computing environment. Acomputer program can be deployed to be executed on one computer or onmultiple computers at one site or distributed across multiple sites andinterconnected by a communication network.

In an example, operations can be performed by one or more programmableprocessors executing a computer program to perform functions byoperating on input data and generating output. Examples of methodoperations can also be performed by, and example apparatus can beimplemented as, special purpose logic circuitry (e.g., a fieldprogrammable gate array (FPGA) or an application-specific integratedcircuit (ASIC)).

The computing system can include clients and servers. A client andserver are generally remote from each other and generally interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. Inembodiments deploying a programmable computing system, it will beappreciated that both hardware and software architectures requireconsideration. Specifically, it will be appreciated that the choice ofwhether to implement certain functionality in permanently configuredhardware (e.g., an ASIC), in temporarily configured hardware (e.g., acombination of software and a programmable processor), or a combinationof permanently and temporarily configured hardware can be a designchoice. Below are set out hardware (e.g., machine 400) and softwarearchitectures that can be deployed in example embodiments.

In an example, the machine 400 can operate as a standalone device or themachine 400 can be connected (e.g., networked) to other machine.

In a networked deployment, the machine 400 can operate in the capacityof either a server or a client machine in server-client networkenvironments In an example, machine 400 can act as a peer machine inpeer-to-peer (or other distributed) network environments. The machine400 can be a personal computer (PC), a tablet PC, a set-top box (STB), aPersonal Digital Assistant (PDA), a mobile telephone, a web appliance, anetwork router, switch or bridge, or any machine capable of executinginstructions (sequential or otherwise) specifying actions to be taken(e.g., performed) by the machine 400. Further, while only a singlemachine 400 is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein.

Example machine (e.g., computer system) 400 can include a processor 402(e.g., a central processing unit (CPU), a graphics processing unit (GPU)or both), a main memory 404 and a static memory 406, some or all ofwhich can communicate with each other via a bus 408. The machine 400 canfurther include a display unit 410, an alphanumeric input device 412(e.g., a keyboard), and a user interface (UI) navigation device 411(e.g., a mouse). In an example, the display unit 810, input device 417and UI navigation device 414 can be a touch screen display. The machine400 can additionally include a storage device (e.g., drive unit) 416, asignal generation device 418 (e.g., a speaker), a network interfacedevice 420, and one or more sensors 421, such as a global positioningsystem (GPS) sensor, compass, accelerometer, or other sensor.

The storage device 416 can include a machine readable medium 422 onwhich is stored one or more sets of data structures or instructions 424(e.g., software) embodying or utilized by any one or more of themethodologies or functions described herein. The instructions 424 canalso reside, completely or at least partially, within the main memory404, within static memory 406, or within the processor 402 duringexecution thereof by the machine 400. In an example, one or anycombination of the processor 402, the main memory 404, the static memory406, or the storage device 416 can constitute machine readable media.

While the machine readable medium 422 is illustrated as a single medium,the term “machine readable medium” can include a single medium ormultiple media (e.g., a centralized or distributed database, and/orassociated caches and servers) that configured to store the one or moreinstructions 424. The term “machine readable medium” can also be takento include any tangible medium that is capable of storing, encoding, orcarrying instructions for execution by the machine and that cause themachine to perform any one or more of the methodologies of the presentdisclosure or that is capable of storing, encoding or carrying datastructures utilized by or associated with such instructions. The term“machine readable medium” can accordingly be taken to include, but notbe limited to, solid-slate memories, and optical and magnetic media.Specific examples of machine readable media can include non-volatilememory, including, by way of example, semiconductor memory devices(e.g., Electrically Programmable Read-Only Memory (EPROM), ElectricallyErasable Programmable Read-Only Memory (EEPROM)) and flash memorydevices; magnetic disks such as internal hard disks and removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks.

The instructions 424 can further be transmitted or received over acommunications network 426 using a transmission medium via the networkinterface device 420 utilizing any one of a number of transfer protocols(e.g., frame relay, IP, TCP, UDP, HTTP, etc.). Example communicationnetworks can include a local area network (LAN), a wide area network(WAN), a packet data network (e.g., the Internet), mobile telephonenetworks (e.g., cellular networks), Plain Old Telephone (POTS) networks,and wireless data networks (e.g., IEEE 802.11 standards family known asWi-Fi®, IEEE 802.16 standards family known as WiMax®), peer-to-peer(P2P) networks, among others. The term “transmission medium” shall betaken to include any intangible medium that is capable of storing,encoding or carrying instructions for execution by the machine, andincludes digital or analog communications signals or other intangiblemedium to facilitate communication of such software.

FIG. 3 is a high level functional block diagram of an embodiment of theinvention.

As shown in FIG. 3, a processor or controller 102 may communicate withthe glucose monitor or device 101, and optionally be insulin device 100.The glucose monitor or device 101 may communicate with the subject 103to monitor glucose levels of the subject 103. The processor orcontroller 102 is configured to perform the required calculations.Optionally, the insulin device 100 may communicate with the subject 103to deliver insulin to the subject 103. The processor or controller 102is configured to perform the required calculations. The glucose monitor101 and the insulin device 100 may be implemented as a separate deviceor as a single device. The processor 102 can be implemented locally inthe glucose monitor 101, the insulin device 100, or a standalone device(or in any combination of two or more of the glucose monitor, insulindevice, or a stand along device). The processor 102 or a portion of thesystem can be located remotely such that the device is operated as atelemedicine device.

Referring to FIG. 4, in its most basic configuration, computing device144 typically includes at least one processing until 150 and memory 146.Depending on the exact configuration and type of computing device,memory 146 can be volatile (such as RAM), non-volatile (such as ROM,flash memory, etc.) or some combination of the two.

Additionally, device 144 may also have other features and/orfunctionality. For example, the device could also include additionalremovable and/or non-removable storage including, but not limited to,magnetic or optical discs or tape, as well as writable electricalstorage media. Such additional storage is the figure by removablestorage 152 and non-removable storage 148. Computer storage mediaincludes volatile and nonvolatile, removable and non-removable mediaimplemented in any method or technology for storage of information suchas computer readable instructions, data structures, program modules orother data. The memory, the removable storage and the non-removablestorage are all examples of computer storage media. Computer storagemedia includes, but is not limited to, RAM, ROM, EEPROM, flash memory orother memory technology CDROM, digital versatile disks (DVD) or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to store the desired information and which can accessed by thedevice. Any such computer storage media may be part of, or used inconjunction with, the device.

The device may also contain one or more communications connections 154that allow the device to communicate with other devices (e.g., othercomputing devices). The communications connections carry information ina communication media Communication media typically embodies computerreadable instructions, data structures, program modules or other data ina modulated data signal such as a carrier wave or other transportmechanism and includes any information delivery media. The term“modulated data signal” means a signal that one or more of itscharacteristics set or changed in such a manner as to encode, execute,or process information in the signal. By way of example, and notlimitation, communication medium includes wired media such as a wirednetwork or direct-wired connection, and wireless media such as radio,RF, infrared and other wireless media. As discussed above, the termcomputer readable media as used herein includes both storage media andcommunication media.

In addition to a stand-alone computing machine, embodiments of theinvention can also be implemented on a network system comprising aplurality of computing devices that are in communication with anetworking means, such as a network with an infrastructure or an ad hocnetwork. The network connector can be wired connections or wirelessconnections. As a way of example, FIG. 4B illustrates a network systemin which embodiments of the invention can be implemented. In thisexample, the network system comprises computer 156 (e.g., a networkserver), network connection means 158 (e.g., wired and/or wirelessconnections), computer terminal 160, and PDA (e.g., a smart-phone) 162(or other handheld or portable device, such as a cell phone, laptopcomputer, tablet computer, GPS receiver, mp3 player, handheld videoplayer, pocket projector, etc. or handheld devices (or non portabledevices) with combinations of such features). In an embodiment, itshould be appreciated that the module listed as 156 may be glucosemonitor device. In an embodiment, it should be appreciated that themodule listed as 156 may be a glucose monitor device and an insulindevice. Any of the components shown or discussed with FIG. 4A may bemultiple in number. The embodiments of the invention can be implementedin anyone of the devices of the system. For example, execution of theinstructions or other desired processing can be performed on the samecomputing device that is anyone of 156, 160, and 162. Alternatively, anembodiment of the invention can be performed on different computingdevices of the network system. For example, certain desired or requiredprocessing or execution can be performed on one of the computing devicesof the network (e.g., server 156 and/or glucose monitor device), whereasother processing and execution of the instruction can be performed atanother computing device (e.g., terminal 160) of the network system, orvice versa. In fact, certain processing or execution can be performed atone computing device (e.g., server 156 and/or glucose monitor device),and the other processing or execution of the instructions can beperformed at different computing devices that may or may not benetworked For example, the certain processing can be performed atterminal 160, while the other processing or instructions are passed todevice 162 where the instructions are executed. This scenario may be ofparticular value especially when the PDA 162 device, for example,accesses to the network through computer terminal 160 (or an accesspoint in an ad hoc network). For another example, software to beprotected can be executed, encoded or processed with one or moreembodiments of the invention. The processed, encoded or executedsoftware can then be distributed to customers. The distribution can bein a form of storage media (e.g., disk) or electronic copy.

FIG. 5 illustrates a system in which one or more embodiments of theinvention can be implemented using a network, or portions of a networkor computers.

FIG. 5 diagrammatically illustrates an exemplary system in whichexamples of the invention can be implemented. In an embodiment theglucose monitor may be implemented by the subject (or patient) at homeor other desired location. However, in an alternative embodiment it maybe implemented in a clinic setting or assistance setting. For instance,referring to FIG. 5, a clinic setup 158 provides a place for doctors(e.g., 164) or clinician/assistant to diagnose patients (e.g., 159) withdiseases related with glucose. A glucose monitoring device 10 (and/orinsulin pump device or pump) can be used to monitor and/or test theglucose levels of the patient It should be appreciated that while onlyglucose monitor device 10 is shown in the figure, the system of theinvention and any component thereof may be used in the manner depictedby FIG. 5. The system or component may be affixed to the patient or incommunication with the patient as desired or required. For example thesystem or combination of components thereof—including a glucose monitordevice 10, a controller 12, or an insulin pump 14, or any other deviceor component—may be in contact or affixed to the patient through tape ortubing or may be in communication through wired or wireless connections.Such monitor and/or test can be short term (e.g., clinical visit) orlong term (e.g., clinical stay or family). The glucose monitoring deviceoutputs can be used by the doctor (clinician or assistant) forappropriate actions, such as insulin injection or food feeding for thepatient, or other appropriate actions. Alternatively, the glucosemonitoring device output can be delivered to computer terminal 168 forinstant or future analyses. The delivery can be through cable orwireless or any other suitable medium. The glucose monitoring deviceoutput from the patient can also be delivered to a portable device, suchas PDA 166. The glucose monitoring device outputs with improved accuracycan be delivered to a glucose monitoring center 172 for processingand/or analyzing. Such deliver can be accomplished in many ways, such asnetwork connection 170, which can be wired or wireless.

In addition to the glucose monitoring device output errors, parametersfor accuracy improvements, and any accuracy related information can bedelivered, such as to computer 168, and/or glucose monitoring center 172for performing error analyses. This can provide a centralized accuracymonitoring and/or accuracy enhancement for glucose centers, due to theimportance of the glucose sensors.

Examples of the invention can also be implemented in a standalonecomputing device associated with the target glucose monitoring device.An exemplary computing device in which examples of the invention can beimplemented is schematically illustrated in FIG. 4A.

The following patents, applications and publications as listed below andthroughout this document are hereby incorporated by reference in theirentirety herein (and are not admitted to be prior art with respect tothe present invention by inclusion in this section).

-   -   [1] Fox K R: The influence of physical activity on mental        well-being. Public Health Nutr 2:411-418, 1999    -   [2] Franco O H, de Laet C, Peeters A, Jonker J, Mackenbach J,        Nusselder W, Effects of physical activity on life expectancy        with cardiovascular disease Arch Intern Med 165:2355-2360, 2005    -   [3] Kujala U M, Kaprio J, Santa S, Koskenvuo M: Relationship of        leisure-time physical activity and mortality, JAMA 279:440-444,        1998    -   [4] A. Brazeau, R. Rabasa-Lohret, I. Strychar, H, Mircescu,        Barriers to physical activity among patients with type 1        diabetes, Diabetes Care 31 (2008)2108-2109.    -   [5] Riddell M C, Iscoe K E, Physical activity, sport, and        pediatric diabetes, Pediatr Diabetes 2006;7(1):60-70.    -   [6] Riddell M, Perkins B. Exercise and glucose metabolism in        persons with diabetes mellitus: perspectives on the role for        continuous glucose monitoring. J Diabetes Sci Technol 2009: 3:        914-923.    -   [7] Ertl A C, Davis S N, 2004 Evidence for a vicious cycle of        exercise and hypoglycemia in type 1 diabetes mellitus, Diabetes        Metab Res Rev 20:124-130    -   [8] Cryer P E, Davis S N, Shamoon H 2003 Hypoglycemia in        diabetes. Diabetes Care26: 1902-1912    -   [9] Goodyear L J, Kahn B B: Exercise, glucose transport, and        insulin sensitivity. Annu Rev Med 1998:49:235-261.    -   [10] Younk L M, Mikeladze M, Tale D. Davis S N: Exerciserelated        hypoglycemia in diabetes mellitus. Expert Rev Endocrinol Metab        2011:6: 93-108.    -   [11] Dohm G L: invited review regulation of skeletal muscle        GLUT-4 expression by exercise. J Appl Physiol 2002;93: 782-787.    -   [12] Brazau A S, Rabasa-Lhorel R, Strychar I, Mircescu H.        Barriers to physical activity among patients with type 1        diabetes. Diabetes Care. 2008:31(11)2108-9.    -   [13] T. Hastie, R. Tibshirani, J, Friedman. The elements of        Statistical Learning Springer series in statistics Springer, New        York, 2001.    -   [14] H, Akaike. A new look at the sutistical model        identification IEEE Trans. Autom. Control. AC-19:716-723, 1974.    -   [15] Hirotugu Akaike. Fitting autoregressive models for        prediction. Annals of the Institute of Statistical Mathematics,        21(1), 1969.    -   [16] Michele Schiavon, Chiara Dalla Man, Yogish C, Kudva, Ananda        Basu, Claudio Cobelli. In Silico Optimization of Basal Insulin        Infusion Rate during Exercise. Implication for Artificial        Pancreas J Diabetes Sci Technol November 2013 vol. 7 no. 6        146I-I469.    -   [17] Kovatchev B P, Breton M, Dalla Man C, Cobelli C. In silico        preclinical trials: a proof of concept in closed-loop control of        type 1 diabetes J Diabetes Sci Technol. 2009;3(1):44-55    -   [18] Dalla Man C, Micheletto F, Dayu L, Breton M, Kovatchev B P,        Cobelli C, The UVA/Padova type 1 diabetes simulator new features        2013, J Diabetes Sci Technol.    -   [19] Paul Berger and Franz Edelman. 1977. IRIS: a        transactions-based DSS for human resources management SIGMIS        Database 8, 3 (January 1977), 22-29.        DOI=10.1145/1017583.1017588.    -   [20] Izak Benbasat, 1977. Cognitive style considerations in DSS        design SIGMIS Database 8, 3 (January 1977), 37-38.        DOI=10.1145/1017583.1017500.    -   [21] Green, C., Yates. R., Raphael, B., and Rosen, C. Research        in Advanced Formal Theorem-Proving Techniques. Technical Note,        AI Center, SRI International, 333 Ravenswood Ave, Menlo Park.        Calif. 94025, June 1969.    -   [22] Richard D. Hackathorn. 1977. Modeling unstructured decision        making. SIGMIS Database 8, 3 (January 1977), 41-42.        DOI=10.1145/1017583.1017593    -   [23] Bonczek, Robert H., Clyde W, Holsapple, and Andrew B.        Whinston. Foundations of Decision Support Systems. Acad. Press,        1981.    -   [24] P. Keen, M. Scott Morton, Decision Support Systems. An        Organizational Perspective, Addison-Wesley Publishing, Reading,        Ma., 1978.    -   [25] Shim, J. P., Merrill Warkentin. James F. Courtney,        Daniel J. Power, Ramesh Sharda, and Christer Carlsson. “Past,        Present, and Future of Dccision Support Technology.” Decision        Support Systems, Decision Support System Directions for the Nest        Decade, 33, no. 2 (June 2002) 111-26. doi:        10.1016/S0167-9236(01)00139-7.    -   [26] Bates D W, Cohen M, Leape L L, Overhage J M, Shabot M M,        Sheridan T J, Reducing the frequency of errors in medicine using        information technology. Am Med Inform Assoc. 2001 July-August,.        8(4):299-308.    -   [27] “Using an Insulin Pump and a CGM.” Diabetes Forecast.        Accessed Mar. 21, 2015.        http://www.diabetesforecast.org/2013/jan/cgms-and-insulin-pumps-2013.html.    -   [28] Teich J M, Wrinn M M, Clinical decision support systems        come of age. MD Comput. 2000 January-February, 17(1)43-6.    -   [29] Bao, L, and S.S Intiller. Activity Recognition from        User-Annotated Acceleration Data, in Proc. Int'l Conf. Pervasive        Comp. 2004: p. 1-17.    -   [30] Lester, J., et al., A Hybrid Discriminative/Generative        Approach for Modeling Human Activities, in Proc. Int'l Joint        Conf. on Artificial Intelligence. 2005: p. 766-772.    -   [31] Lukowicz, P., et al., Recognizing Workshop Activity Using        Body Worn Microphone and Accelerometers, in Proc Int'l Conf.        Pervasive Comp. 2004: p. 18-32.    -   [32] Maurer, U., et al., Activity Recognition and Monitoring        Using Multiple Sensors on Different Body Positions, in Proc.        Int'l Workshop on Wearable and Implantable Body Sensor        Networks. 2006. p. 113-116.

1[33] Keytel L R1, Goedecke J H, Noakes T D, Hiiloskorpi H, Laukkanen R,van der Merwe L., Lambert E V. Prediction of energy expenditure fromheart rate monitoring during submaximal exercise. J Sports Sci. 2005Mar.; 23(3);289-97.

-   -   [34] Rennie K L I, Hennings S J, Mitchell J, Wareham NJ.        Estimating energy expenditure by heart-rate monitoring without        individual calibration. Med Sci Sports Exerc. 2001 Jun.;        33(6);939-45.    -   [35] J. R. Karp, “HR training for improved running performance.”        Track Coach. Track and Field News, USA, pp. 5035-5039, 2001.

1[36] L. Somanathan, and I. Khalil, “Fitness monitoring system based onHR and Sp02 level.” Proc. of the 10th IEEE International Conference onITAB, Corfu, pp. 1-5, November. 2010.

-   -   [37] Marc D. Breton, Sue A, Brown, Colleen Hughes Karvetski,        Laura Kollar, Katarina A, Topchyan, Stacey M, Anderson, and        Boris P, Kovatchev Adding Heart Rate Signal to a        Control-to-Range Artificial Pancreas System Improves the        Protection Against Hypoglycemia During Exercise in Type 1        Diabetes DIABETES TECHNOLOGY & THERAPEUTICS Volume 16, Number 8,        2014^(a) Mary Ann Licbert, Inc. DOI: 10, 1089/dta, 2013.0333    -   [38] Parkka, M, Ermes, P, Korpipaa. J, Mantyjarvi, J, Peltola,        and I. Korhonen, “Activity Classification Using Realistic Data        from Wearable Sensors.” IEEE Trans. Information Technology in        Biomedicine, vol. 10, no. 1, pp. 119-128, January. 2006.    -   [39] E.M. Tapia and S. Intille, “Real-Time Recognition of        Physical Activities and Their Intensities Using Wireless        Acceleromeiers and a Heart Rate Monitor,” Proc. Int'l Symp.        Wearable Computers (ISWC), pp. 1-4, 2007    -   [40] Matthew Stenerson, Fraser Cameron, Darrell M, Wilson,        Breanne Harris, Shelby Payne, B, Wayne Bequette, and Bruce A,        Buckingham, The Impact of Accelerometer and Heart Rate Data on        Hypoglycemia Mitigation in Type 1Diabetes, Journal of Diabetes        Science and Technology 2014, Vol. 8(1) 64-69®2014 Diabetes        Technology Society DOI: 10.1177/193229681316208    -   [41] Matthew Stenerson, Fraser Cameron, Shelby R, Payne,        Sydney L. Payne, Trang T. Ly, MBBS, FRACP, Darrell M Wilson,        Bruce A. Buckingham. The Impact of Accelerometer Use in        Exercise-Associated Hypoglycemia Prevention in Type 1 Diabetes,        J Diabetes Sci Technol Sep. 17, 2014 1932296814551045    -   [42] S. D Patek, L. Magni. E. Dassan. C. Hughes-Karvelski, C.        Toffanin. G. De Nicolao, S. Del Favero, M. Breton, C. Dalla        Man, E. Renard, H. Zisser, F. J. Doyle, Ill, C. Cobelli        and B. P. Kovatchev, and International Artificial Panceas (iAP)        Study GroupModular Closed-Loop Control of Diabetes. IEEE        TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 59, NO. 11,        NOVEMBER 2012    -   [43] Buckingham B, Cobry E, Clinton P, Gage V, Caswell K,        Kunselman E, Cameron F, Chase H P. Preventing hypoglycemia using        predictive alarm algorithms and insulin pump suspension.        Diabetes Technol Therapeutics 2009;11(2):93-97.    -   [44] Cengiz E, Swan K L, Tamborlane W V, Steil G M, Steffen A T,        Weinzimer S A, Is an automatic pump suspension feature safe for        children with type 1 diabetes? An exploratory analysis with        closed-loop system Diabetes Technol Therapeutics        2009;11(4):207-210.    -   [45] Zisser H, Robinson L, Bevier W, Dassau E, Ellingsen C,        Doyle F J III, Jovanovie L, Bolus calculator: A review of four        “smart” insulin pumps. Diabetes Technol Ther 2008;10(6): 441-444        [PubMed: 19049372]    -   [46] Patek, S D.; Breton, M D.; Hughes, C.; Kovatchev, BP .        Control of hypoglycemia via estimation of active insulin,        glucose forecasts, and risk-based insulin reduction. Proc. 2nd        Advanced Technol. Treatment for Diabetes, Athens, Greece. 2009.    -   [47] Kowalski A J, Can we really close the loop and how soon?        Accelerating the availability of an artificial pancreas: A        roadmap to better diabetes outcomes. Diabetes Technol        Therapeutics 2009;11:S113-S119.    -   [48] Boris P. Kovatchev, PHD1, Erik Otto, MBA2, Daniel Cox,        PHD1, Linda Gonder-Frederick, PHD1 and William Clarke, Md.,        “Evaluation of a New Measure of Blood Glucose Variability in        Diabetes”, 10.2337/dc06-1085Diabetes Care November 2006 vol. 29        no. 11 2433-2438.

The devices, systems, non-transitory computer readable medium, andmethods of various embodiments of the invention disclosed herein mayutilize aspects disclosed in the following references, applications,publications and patents and which are hereby incorporated by referenceherein in their entirely (and which are not admitted to be prior artwith respect to the present invention by inclusion in this section):

-   -   a. U.S. patent application Ser. No. 14/419,375 entitled        “Computer Simulation for Testing and Monitoring of Treatment        Strategies for Stress Hyperglycemia”, filed Feb. 3, 2015.    -   b. International Patent Application No. PCT/US2013/053664        entitled “Computer Simulation for Testing and Monitoring of        Treatment Strategies for Stress Hyperglycemia”, filed Aug. 5,        2013; International Patent Application Publication No. WO        2014/022864, Feb. 6. 2014.    -   c. International Patent Application No PCT/US2015/010167        entitled “Central Data Exchange Node For System Monitoring and        Control of Blood Glucose Levels in Diabetic Patients”, filed        Jan. 5, 2015.    -   d International Patent Application No. PCT/US2014/045393        entitled “Simulation of Endogenous and Exogenous        Glucose/Insulin/Glucagon Interplay in Type 1 Diabetic Patients”,        filed Jul. 3, 2014.    -   e. U.S. patent application Ser. No. 14/266,612 entitled “Method,        System and Computer Program Product for Real-Time Detection of        Sensitivity Decline in Analyte Sensors”, filed Apr. 30, 2014, US        Patent Application Publication No 2014/0244216, Aug. 28, 2014.    -   f. U.S. patent application Ser. No. 13/418,305 entitled “Method,        System and Computer Program Product for Real-Time Defection of        Sensitivity Decline in Analyte Sensors”, tiled Mar. 12, 2012;        U.S. Pat. No. 8,718,958, issued May 6, 2014.    -   g. International Patent Application No PCT/US2007/082744        entitled “Method, System and Computer Program Product for        Real-Time Detection of Sensitivity Decline in Analyte Sensors”,        filed Oct. 26, 2007; International Patent Application        Publication No WO/2008/052199, May 2, 2008.    -   h. U.S. patent application Ser. No. 11/925,689 entitled “Method,        System and Computer Program Product for Real-Time Dcection of        Sensitivity Decline in Analyte Sensors”, filed Oct. 26, 2007;        U.S. Pat. No. 8,135,548, issued Mar. 13, 2012.    -   i. U.S. patent application Ser. No. 14/241,383 entitled “Method        System and Computer Readable Medium for Adaptive Advisory        Control of Diabetes”, filed Feb. 26, 2014.    -   j. International Patent Application No PCT/US2012/052422        entitled “Method, System and Computer Readable Medium for        Adaptive Advisory Control of Diabetes”, filed Aug. 26, 2012;        International Patent Application Publication No. WO 2013/032065,        Mar. 7, 2013.    -   k. International Patent Application No. PCT/US2014/017754        entitled “Method and System for Model-Based Tracking of Changes        in Average Glycemia in Diabetes”, filed Feb. 21, 2014;        International Patent Application Publication No. WO 2014/130841;        Aug. 28, 2014.    -   l. U.S. patent application Ser. No 14/128,922 entitled “Unified        Platform For Monitoring and Control of Blood Glucose Levels in        Diabetic Patients”, filed Dec. 23, 2013; U.S. Patent Application        Publication No. 2015/0018633, Jan. 15, 2015.    -   m. International Patent Application No. PCT/US2012/043910        entitled “Unified Platform For Monitoring and Contral of Blood        Glucose Levels in Diabetic Patients”, filed Jun. 23, 2012;        International Patent Application Publication No. WO 2012/178134,        Dec. 27, 2012.    -   n. U.S. patent application Ser. No. 14/128,811 entitled “Methods        and Apparatus for Modular Power Management and Protection of        Critical Services in Ambulatory Medical Devices”, filed Dec. 23,        2013; US Patent Application Publication No. 2014/0215239; Jul.        31, 2014.    -   o. International Patent Application No PCT/US2012/043883        entitled “Methods and Apparatus for Modular Power Management and        Protection of Critical Services in Ambulatory Medical Devices”,        filed Jun. 22, 2012; International Patent Application        Publication No. WO 2012/178113, Dec. 27, 2012.    -   p. U.S. patent application Ser. No. 14/015,831 entitled        “CGM-Based Prevention of Hypoglycemia Via Hypoglycemia Risk        Assessment and Smooth Reduction Insulin Delivery”, filed Aug.        30, 2013.    -   q. U.S. patent application Ser. No. 13/203,469 entitled        “CGM-Based Prevention of Hypoglycemia via Hypoglycemia Risk        Assessment and Smooth Reduction Insulin Delivery”, filed Aug.        25, 2011; U.S. Pat. No. 8,.562,587, issued Oct. 22, 2013.    -   r. International Patent Application No. PCT/US2010/025405        entitled “CGM-Based Prevention of Hypoglycemia via Hypoglycemia        Risk Assessment and Smooth Reduction Insulin Delivery”, filed        Feb. 25, 2010; International Patent Application Publication No.        WO 2010/099313, Sep. 2, 2010.    -   s. International Patent Application No. PCT/US2013/042745        entitled “Insulin-PramIintide Compositions and Methods for        Making and Using Them”, filed May 24, 2013, International        Application Publication No. WO 2013/177565, Nov. 28, 2013.    -   t. U.S. patent application Ser. No. 13/637,359 entitled “Method,        System, and Computer Program Product for Improving the Accuracy        of Glucose Sensors Using Insulin Delivery Observation in        Diabetes”, filed Sep. 25, 2012; U.S. Patent Application        Publication No 2013/0070613; Mar. 28, 2013.    -   u. International Patent Application No. PCT/US2011/029703        entitled “Method, System, and Computer Program Product for        Improving the Accuracy of Glucose Sensors Using Insulin Delivery        Observation in Diabetes”, filed Mar. 24, 2011; International        Patent Application Publication No. WO 2011/119832, Sep. 20,        2011.    -   v. U.S. patent application Ser. No 13/634,040 entitled “Method        and System for the Safety, Analysis, and Supervision of Insulin        Pump Action and Other Modes of Insulin Delivery in Diabetes”,        filed Sep. 11, 2012; U.S. Patent Application Publication No.        2013/0116640, May 9, 2013.    -   w. International Patent Application No. PCT/US2011/028163        entitled “Method and System for the Safety. Analysis, and        Supervision of Insulin Pump Action and Other Modes of Insulin        Delivery in Diabetes”, filed Mar. 11, 2011; International Patent        Application Publication No. WO 2011/112074, Sep. 15, 2011.    -   x. U.S. patent application Ser. No 13/394,001 entitled “Tracking        the Probability for Imminent Hypoglycemia in Diabetes from        Self-Monitoring Blood Glucose (SMBO) Data”, filed Mar. 2, 2012;        US Patent Application Publication No. 2012/0101361, Jul. 26,        2012.    -   y. International Patent Application No PCT/US2010/047711        entitled “Tracking the Probability for Imminent Hypoglycemia in        Diabetes from Self-Monitoring Blood Glucose (SMBG) Data”, filed        Sep. 2, 2010; International Patent Application Publication No.        WO 2011/028925, Mar. 10, 2011.    -   z. U.S. patent application Ser. No. 13/393,647 entitled “System,        Method and Computer Program Product for Adjustment of Insulin        Delivery (AID) in Diabetes Using Nominal Open-Loop Profiles”,        filed Mar. 1, 2012; US Patent Application Publication No.        2012/0245556, Sep. 27, 2012.    -   aa. International Patent Application No. PCT/US2010/047386        entitled “System, Method and Computer Program Product for        Adjustment of Insulin Delivery (AID) in Diabetes Using Nominal        Open-Loop Profiles”, filed Aug. 31, 2010; International        Application Publication No. WO 2011/028731, Mar. 10, 2011.    -   bb. U.S. patent application Ser. No. 13/380,839 entitled        “System, Method, and Computer Simulation Environment for In        Silico Trials in Pre-Diabetcs and Type 2 Diabetes”, filed Dec.        25, 2011; US Patent Application Publication No. 2012/0130698,        May 24, 2012.    -   cc. International Patent Application No. PCT/US2010/040097        entitled “System, Method, and Computer Simulation Environment        for In Silico Trials in Prediabetes and Type 2 Diabetes”, filed        Jun. 25, 2010; International Applicaiion Publication No. WO        2010/151834, Dec. 29, 2010.    -   dd. U.S patent application Ser. No. 13/322,943 entitled “System        Coordinator and Modular Architecture for Open-Loop and        Closed-Loop Control of Diabetes”, filed Nov. 29, 2011; US.        Patent Application Publication No. 2012/0078067, Mar. 29, 2012.    -   ee. International Patent Application No. PCT/US2010/036629        entitled “System Coordinator and Modular Architecture for        Open-Loop and Closed-Loop Control of Diabetes”, filed May 28,        2010; International Patent Application Publication No. WO        2010/138848, Dec. 2, 2010.    -   ff. U.S. patent application Ser. No. 13/131,467 entitled        “Method, System, and Computer Program Product for Tracking of        Blood Glucose Variability in Diabetes”, filed May 26, 2011; U.S.        Patent Application Publication No. 2011/0264378, Oct. 27, 2011.    -   gg. International Patent Application No. PCT/US2009/065725        entitled “Method, System, and Computer Program Product for        Tracking of Blood Glucose Variability in Diabetes”, filed Nov.        24, 2000; International Patent Application Publication No WO        2010/062898, Jun. 3, 2010.    -   hh. U.S. patent application Ser. No 12/975,580 entitled “Method,        System, and Computer Program Product for the Evaluation of        Clycemic Control in Diabetes from Self-Monitoring Data”, filed        Dec. 22, 2010; U.S. Patent Application Publication No.        2012/0004512, Jan. 5, 2012.    -   ii. U.S. patent application Ser. No. 11/305,946 entitled        “Method, System, and Computer Program Product for the Evaluation        of Glycemic Control in Diabetes from Self-Monitoring Data”,        filed Dec. 19, 2005; U.S. Pat. No. 7,874,985, issued Jan. 25,        2011.    -   jj. U.S. patent application Ser. No. 10/240,228 entitled        “Method, System, and Computer Program Product for the Evaluation        of Glycemic Control in Diabetes from Self-Monitoring Data”,        filed Sep. 26, 2002; U.S. Pat. No. 7,025,425, issued Apr. 11,        2006.    -   kk. International Patent Application No. PCT/US2001/009884        entitled “Method, System, and Computer Program Product for the        Evaluation of Glycemic Control in Diabetes”, filed Mar. 29,        2001; International Application Publication No WO 2001/72208;        Oct. 4, 2001.    -   ll. U.S. patent application Ser. No. 12/674,348 entitled        “Method, Computer Program Product and System for Individual        Assessment of Alcohol Sensitivity”, filed Feb. 19, 2010; U.S.        Patent Application Publication No. 2011/0264374, Oct. 27, 2011.    -   mm. International Patent Application No. PCT/US2008/073738        entitled “Method, Computer Program Product and System for        Individual Assessment of Alcohol Sensitivity”, filed Aug. 20,        2008; International Patent Application Publication No. WO        2009/026381, Feb. 26, 2009.    -   nn. U.S. patent application Ser. No. 12/665,149 entitled        “Method, System and Computer Program Product for Evaluation of        Insulin Sensitivity, Insulin/Carbohydrate Ratio, and Insulin        Correction Factors in Diabetes from Self-Monitoring Data”, filed        Dec. 17, 2009; US Patent Application Publication No        2010/0198520, Aug. 5, 2010.    -   oo. International Patent Application No. PCT/US2008/069416        entitled “Method, System and Computer Program Product for        Evaluation of Insulin Sensitivity, Insulin/Carbohydrate Ratio,        and Insulin Correction Factors in Diabetes from Self-Monitoring        Data”, filed Jul. 8, 2008; International Patent Application        Publication No. WO 2009/009528, Jan. 15, 2009.    -   pp U.S. patent application Ser. No. 12/664,444 entiled “Method,        System and Computer Simulation Environment for Testing of        Monitoring and Control Strategies in Diabetes”, filed Dec. 14,        2009; US Patent Application Publication No. 2010/0179768, Jul.        15, 2010.    -   qq. International Patent Application No. PCT/US2008/067725        entitled “Method, System and Computer Simulation Environment for        Testing of Monitoring and Control Strategies in Diabetes”, filed        Jun. 20, 2008; International Patent Application Publication No.        WO 2008/157781, Dec. 24, 2008.    -   tt. U.S patent application Ser. No. 12/516,044 entitled “Method,        System, and Computer Program Product for the Detection of        Physical Activity by Changes in Heart Rate, Assessment of Fast        Changing Metabolic States, and Applications of Closed and Open        Control Loop in Diabetes”, filed May 22, 2009; U.S. Pat. No.        8,585,593, issued Nov. 19, 2013.    -   ss. International Patent Application No. PCT/US2007/085588        entitled “Method, System, and Computer Program Product for the        Detection of Physical Activity by Changes in Heart Rate,        Assessment of Fast Changing Metabolic States, and Applications        of Closed and Open Control Loop in Diabetes”, filed Nov. 27,        2007; International Patent Application Publication No.        WO2008/067284, Jun. 5, 2008.    -   tt. U.S. patent application Ser. No. 12/159,891 entitled        “Method, System and Computer Program Product for Evaluation of        Blood Glucose Variability in Diabetes from Self-Monitoring        Data”, filed Jul. 2, 2008; U.S. Patent Application Publication        2009/0171589, Jul. 2, 2009.    -   uu. International Patent Application No. PCT/US2007/000370        entitled “Method, System and Computer Program Product for        Evaluation of Blood Glucose Variability in Diabetes from        Self-Monitoring Data”, filed Jan. 5, 2007, International        Application Publication No. WO 2007/081853, Jul. 19, 2007.    -   vv. U.S. patent application Ser. No. 12/065,257 entitled        “Accuracy of Continuous Glucose Sensors”, filed Feb. 28, 2008;        US Patent Application Publication No. 2008/0314395, Dec. 25,        2008.    -   ww. International Patent Application No. PCT/US2006/033724        entitled “Method for Improvising Accuracy of Continuous Glucose        Sensors and a Continuous Glucose Sensor Using the Same”, filed        Aug. 29, 2006; International Application Publication No. WO        2007027691, Mar. 8, 2007.    -   xx. U.S. patent application Ser. No. 11/943,226 entitled        “Systems, Methods and Computer Program Codes for Recognition of        Patterns of Hyperglycemia and Hypoglycemia, Increased Glucose        Variability, and Ineffective Self-Monitoring in Diabetes”, filed        Nov. 20, 2007; US Patent Application Publication No.        2008/0154513, Jun. 26, 2008.    -   yy. U.S. patent application Ser. No. 11/578,831 entitled        “Method, System and Computer Program Product for Evaluating the        Accuracy of Blood Glucose Monitoring Sensors/Devices”, filed        Oct. 18, 2006; U.S. Pat. No. 7,815,569, issued Oct. 19 ,2010.    -   zz. International Patent Application No. US2005/013792 entitled        “Method, System and Computer Program Product for Evaluating the        Accuracy of Blood Glucose Monitoring Sensors Devices”, filed        Apr. 21, 2005; International Application Publication No. WO        2005/106017, Nov. 10, 2005.    -   aaa. U.S. patent application Ser. No. 10/592,883 entitled        “Method, Apparatus, and Computer Program Product for Stochastic        Psycho-physiological Assessment of Attentional Impairments”,        filed Sep. 15, 2006; U.S. Pat. No. 7,761,144, issued Jul. 20,        2010.    -   bbb. U.S. patent application Ser. No. 10/524,094 entitled        “Method, System, And Computer Program Product For The Processing        Of Self-Monitoring Blood Glucose (SMBG) Data To Enhance Diabetic        Self-Management”, filed Feb. 9, 2005; U.S. Pat. No. 8,538,703,        issued Sep. 17, 2013.    -   ccc. International Patent Application No. PCT/US2003/02503        entitled “Managing and Processing Self-Monitoring Blood        Glucose”, filed Aug. 8, 2003; International Application        Publication No. WO 2001/72208, Oct. 4, 2001.    -   ddd. International Patent Application No PCT/US2002/005676        entitled “Method and Apparatus for the Early Diagnosis of        Subacute, Potentially Catastrophic Illness”, filed Feb. 27,        2002; International Application Publication No WO 2002/67776,        Sep. 6, 2002.    -   eee. U.S. patent application Ser. No. 9/793,653 entitled “Method        and Apparatus for the Early Diagnosis of Subacute, Potentially        Catastrophic Illness”, filed Feb. 27, 2001; U.S. Pat. No.        6,804,551, issued Oct. 12, 2004.    -   fff. U.S. patent application Ser. No. 10/069,674 entitled        “Method and Apparatus for Predicting the Risk of Hypoglycemia”,        filed Feb. 22, 2002; U.S. Pat. No. 6,923,763, issued Aug. 72,        2005.    -   ggg. International Patent Application No. PCT/US00/22886        entitled “METHOD AND APPARATUS FOR PREDICTING THE RISK OF        HYPOGLYCEMIA”, filed Aug. 21, 2000; International Application        Publication No WO 2001/13786, Mar. 1, 2001.    -   hhh. U.S. patent application Ser. No. 12/665,420 entitled “LQG        Artificial Pancreas Control System and Related Method”, filed        Dec. 18, 2009; U.S. Patent Application Publication No.        2010/0249561, Sep. 30, 2010.    -   iii. International Patent Application No. PCT/US2008/067723        entitled “LQG Artificial Pancreas Control System and Related        Method”, filed Jun. 20, 2008; International Patent Application        Publication No. WO 2008/157780, Dec. 24, 2008.

In summary, while the present invention has been described with respectto specific embodiments, many modifications, variations, alterations,substitutions, and equivalents will be apparent to those skilled in theart. The present invention is not to be limited in scope by the specificembodiment described herein. Indeed, various modifications of thepresent invention, in addition to those described herein, will beapparent to those of skill in the art from the foregoing description andaccompanying drawings. Accordingly, the invention is to be considered aslimited only by the spirit and scope of the disclosure, including allmodilications and equivalents.

Still other embodiments will become readily apparent to those skilled inthis art from reading the above-recited detailed description anddrawings of certain exemplary embodiments. It should be understood thatnumerous variations, modifications, and additional embodiments arepossible, and accordingly, all such variations, modifications, andembodiments are to be regarded as being within the spirit and scope ofthis application. For example, regardless of the content of any portion(e.g., title, field, background, summary, abstract, drawing figure, etc)of this application, unless clearly specified to the contrary, there isno requirement for the inclusion in any claim herein or of anyapplication claiming priority hereto of any particular described orillustrated activity or clement, any particular sequence of suchactivities, or any particular interrelationship of such elements.Moreover, any activity can be repeated, any activity can be performed bymultiple entities, and/or any element can be duplicated. Further, anyactivity or element can be excluded, the sequence of activities canvary, and/or the interrelationship of elements can vary. Unless clearlyspecified to the contrary, there is no requirement for any particulardescribed or illustrated activity or element, any particular sequence orsuch activities, any particular size, speed, material, dimension orfrequency, or any particularly interrelationship of such elements.Accordingly, the descriptions and drawings are to be regarded asillustrative in nature, and not as restrictive. Moreover, when anynumber or range is described herein, unless clearly stated otherwise,that number or range is approximate. When any range is described herein,unless clearly slated otherwise, that range includes all values thereinand all sub ranges therein. Any Information in any material (e.g., aUnited States/foreign patent, United States/foreign patent application,book, article, etc.) that has been incorporated by reference herein, isonly incorporated by reference to the extent that no conflict existsbetween such information and the other statements and drawings set forthherein. In the event of such conflict, including a conflict that wouldrender invalid any claim herein or seeking priority hereto, then anysuch conflicting information in such incorporated by reference materialis specifically not incorporated by reference herein.

1. A system for generating a hypoglycemia risk signal associated withexercise-induced hypoglycemia comprising a processor configured to:obtain a blood glucose signal (BG_(start)), a ratio of absolute insulinon board over total daily insulin signal (lOB_(abs)/TDI), and an initialglyceniic slope signal (S₀); generate a hypoglycemia risk signal basedon a hypoglycemia prediction algorithm that determines the probabilityof a user being hypoglycemic during or after exercise based on theobtained BG_(star)t, IOB_(abs)/TDI and S₀.
 2. The system of claim 1,wherein the processor is configured to determine a hypoglycemia riskstate with a classifier algorithm that classifies the hypoglycemia risksignal to identify an actionable hypoglycemia risk statebased on apredefined threshold
 3. The system of claim 1, wherein Ihc hypoglycemiaprediction algorithm is:${{Logit}(P)} = {{\beta \; 0} + {\beta_{1} \cdot {BG}_{start}} + {\beta_{2} \cdot \frac{{IOB}_{abs}}{TDI}} + {\beta_{3} \cdot S_{0}}}$${Where}\left\{ \begin{matrix}{{{Logit}(P)} = {{Log}\left( \frac{P}{1 - P} \right)}} \\{P = \frac{e^{{\beta \; 0} + {\beta_{1} \cdot {BG}_{start}} + {\beta_{2} \cdot \frac{{IOB}_{abs}}{TDI}} + {\beta_{3} \cdot S_{0}}}}{1 + e^{{\beta \; 0} + {\beta_{1} \cdot {BG}_{start}} + {\beta_{2} \cdot \frac{{IOB}_{abs}}{TDI}} + {\beta_{3} \cdot S_{0}}}}}\end{matrix} \right.$ wherein β0, β₁, β₂, and β₃ are predeterminedconstants.
 4. The system of claim 2, wherein the classifier algorithmis: ${{Logit}(P)} = \left\{ \begin{matrix}{1,} & {{{Logit}(P)} \geq {DET}_{thresh}} \\{0,} & {{{Logit}(P)} < {DET}_{thresh}}\end{matrix} \right.$ wherein DET_(thresh) is a predetermined constant.5. The system of claim 4, wherein DET_(thresh) is 0.4.
 6. The system ofclaim 1, wherein the S₀ is obtained based on the blood glucose signal.7. The system of claim 2, comprising: a display in communication withthe processor, the display being configured to present an alert when thehypoglycemia risk slate indicates a risk of hypoglycemia.
 8. The systemof claim 1, comprising: a continuous blood glucose monitoring sensor incommunication with the processor, the continuous blood glucosemonitoring sensor configured to generate the blood glucose signal. 9.The system of claim 1, comprising: an insulin pump in communication withthe processor, the insulin pump configured to deliver glucose to a userwhen the hypoglycemic risk state indicates a risk of hypoglycemia duringor after exercise.
 10. The system of claim 1, wherein the processor isconfigured to communicate an instruction to the user to consume adetermined amount of carbohydrate prior to or during the exercise whenthe hypoglycemia risk stateindicsles a risk of hypoglycemia during orafter exercise.
 11. The system of claim 1, comprising: at least onesensor configured to delect when the user begins to exercise
 12. Thesystem of claim 11, wherein the at least one sensor includes one or moreof a heartrate sensor and an accelerometer.
 13. The system of claim 11,wherein in response to the at least one sendsor detecting that the userhas begun to exercise, the processor is configured to generate an alertwhen the hypoglycemia risk state indicates a risk of hypoglycemia duringor after exercise.
 14. The system of claim 1, wherein the processor isconfigured to: obtain from a user one or more additional signalscorresponding to a type of exercise, an intensity of exercise, or aduration of exercise, and wherein the hypoglycemic prediction algorithmdetermines the probability of the user being hypoglycemic during orafter exercise based at least in part on the one or more additionalsignals.
 15. A computer-implemented method for generating a hypoglycemiarisk signal associated with exercise-induced hypoglycemia, the methodcomprising obtaining a blood glucose signal (BG_(start)), a ratio ofabsolute insulin on board over total daily insulin signal(IOB_(abs)/TDI), and an initial glycemic slope signal (S₀); generating ahypoglycemia risk signal based on a hypoglycemia prediction algorithmthat determines the probability of a user being hypoglycemic during orafter exercise based on the obtained BG_(star)t, IOB_(abs)/TDI and S₀.16. The method of claim 15, comprising: determining a hypoglycemia riskstatewith a classifier algorithm that classifies the hypoglycemia risksignal to identify an actionable hypoglycemia risk state based on apredefined threshold.
 17. The method of claim 15, wherein thehypoglycemia prediction algorithm is:${{Logit}(P)} = {{\beta \; 0} + {\beta_{1} \cdot {BG}_{start}} + {\beta_{2} \cdot \frac{{IOB}_{abs}}{TDI}} + {\beta_{3} \cdot S_{0}}}$${Where}\left\{ \begin{matrix}{{{Logit}(P)} = {{Log}\left( \frac{P}{1 - P} \right)}} \\{P = \frac{e^{{\beta \; 0} + {\beta_{1} \cdot {BG}_{start}} + {\beta_{2} \cdot \frac{{IOB}_{abs}}{TDI}} + {\beta_{3} \cdot S_{0}}}}{1 + e^{{\beta \; 0} + {\beta_{1} \cdot {BG}_{start}} + {\beta_{2} \cdot \frac{{IOB}_{abs}}{TDI}} + {\beta_{3} \cdot S_{0}}}}}\end{matrix} \right.$ wherein β0, β₁, β₂, and β₃ are predeterminedconstants.
 18. The method of claim 16, wherein the classifier algorithmis: ${{Logit}(P)} = \left\{ \begin{matrix}{1,} & {{{Logit}(P)} \geq {DET}_{thresh}} \\{0,} & {{{Logit}(P)} < {DET}_{thresh}}\end{matrix} \right.$ wherein DET_(thresh) is a predetermined constant.19. The method of claim 16, comprising: generating an alert when thehypoglycemia risk state indicates a risk of hypoglycemia during or afterexercise.
 20. The method of claim 16, comprising: communicating aninstruction to an insulin pump when the hypoglycemia risk stateindicates a risk of hypoglycemia during or after exercise.